The cutaneous T-cell lymphomas (CTCL) including Mycosis fungoides (MF) and Sezary syndrome (SS) are indolent lymphomas that progress in stages, starting with skin lesions, sometimes proceeding through a leukemic phase with circulating tumor cells and eventually spreading to the visceral organs. Treatments for CTCL vary in efficacy even for patients with what appears to be similar level of disease, emphasizing the likely existence of undetectable heterogeneity. These characteristics added to a the availability of a large archive of patient samples make it a good candidate for tumor staging by molecular profiles. SS, the leukemia form of CTCL will be the initial focus of these studies as it provides easy access to large numbers of purified malignant cells. RNA from 10 patients, with diverse patterns of disease presentation and progression, will be analyzed during the first year against arrays of cDNA probes for 20,000 sequence verified Unigene clusters in order to determine the global gene expression patterns of these cells. Samples will be selected from newly diagnosed SS patients and from an archive of viably frozen SS cells including samples collected at progressive stages of disease over a period of greater than 10 years. Since CTCL cells represent Th-2 T-cells, RNA from healthy donor PBL, stimulated to develop a TH-2 phenotype will be used as controls. Genes that are over or under-expressed in patient RNAs, compared to controls, will be candidate tumor markers for a reduced panel of genes that will be used to screen a larger group of patient samples. In the second phase of the study, 100 patients will be selected for gene expression studies with a reduced panel of 1000-2000 genes. These expression profiles will be analyzed, using statistical techniques, to identify groups of genes that behave in a similar fashion in subsets of patients. The results of these analyses will be a putative diagnostic panel of genes whose expression levels describe classes of tumors. The correlation between expression levels and tumor groups will be confirmed using alternative methods for measuring gene expression. Finally, clinical information from patient histories will be compared with tumors clustered by gene expression levels to determine whether important clinical outcomes, e.g., responsiveness to treatment, can be predicted from the specific gene expression patterns. Concurrent with the above studies, samples from patients with MF, the skin-associated early form of CTCL, will be queried with the panel of genes identified as being diagnostic for SS to determine whether the same genes are also sufficient to characterize different classes of MF. If novel gene clusters are found, they will be added to the data base of candidate markers. If not, up to 10 MF patients will be analyzed on 20,000-gene filters for genes whose expression pattern distinguishes MF from SS patients. If found, these will be added to the panel of candidate SS genes. Finally, techniques will be developed to assay expression profiles in a clinical setting.